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I’m ready to retire. In my younger days, I remember a few pivotal moments for me as a young nerd. Active Server Pages. COM components. VB6. I know these are laughable today but back then it was the greatest thing in the world to be able to call server-side commands. It kept me up nights trying to absorb it all. Fast forward decades and Claude Code is giving me that same energy and drive. I love it. It feels like it did back then. I’m chasing the midnight hour and not getting any sleep.

I am VE3HWO. I hold a basic with honours and advanced qualifications in Canada. Hoping to connect with other hams on HN. 73

so i’ve been experimenting with a small AI-based project recently and started thinking about logging around prompts, responses, and model calls etc etc.

for traditional systems observability tools handle most of this, but with LLM-based apps it feels less clear what the standard approach is, especially if you need proper audit trails for debugging or compliance.

curious how teams are handling this in production

are people mostly building their own logging pipelines, or are there reliable tools/platforms that help with storing and auditing LLM interactions?

AI agents are increasingly performing automated web research — browsing pages, following links, and sometimes clicking results as part of information gathering.

There's a small but potentially significant side effect: these systems can end up clicking paid advertisements.

Most online advertising runs on a pay-per-click (PPC) model. When a human clicks an ad, there's at least some level of commercial intent. When an AI agent clicks an ad during automated research, there's zero purchase intent — but the advertiser may still be charged.

At the individual level this is negligible. But AI agents are beginning to operate at scale — millions of automated queries. The cumulative effect on advertisers, particularly small businesses with tight budgets, could become meaningful.

This raises a few questions:

1. Should AI agents avoid clicking sponsored/promoted results by default? 2. Should browsers and agent frameworks detect labels like "Sponsored," "Promoted," or "Ad" and skip those results unless explicitly opted in?

Secondary effects worth considering: unintended ad spend for advertisers, distortion of click-through analytics, and reduced research quality (ad placement reflects budget more than relevance).

The web's ad-funded model depends on clicks having some commercial signal. If AI agents start generating ad clicks at scale with no purchase intent, it could quietly distort that ecosystem.

Curious how engineers and AI developers here think about this — both from an agent design standpoint and from the web economics angle.

I've been on HN under different aliases since 2010 and over the last couple of years I feel like the quality of HN has nosed dived and so has my enjoyment.

For the first time ever I questioned today whether I should continue to use HN anymore so I'm writing this partly to explore my own thoughts and to see if anyone else feels similarly.

1. AI, AI, AI.

I get it. AI is the big thing right now, but I find AI posts fundamentally less interesting than the traditional tech content that used to be posted here. A post containing someone's qualitative opinion on how different AI models compare when drawing pelicans simply isn't as technically interesting as something like this, https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times-by-70/

2. Does any build startups here anymore?

Again, I get it. I largely quit trying to bootstrap my own startup ideas in the late 2010s. The industry became too competitive for a solo founder without significant financial backing to have much of a chance of success. And today it's even harder. But I think this has changed HN from a place where you used to frequently see people launching cool new projects to a place where people just discuss the latest big tech AI model launch.

3. Politicisation and intolerance

One of the things I've always liked about HN was that it's a very open minded place. And it still is in many ways, especially when compared to other platforms like X and Reddit, but even here I've noticed comments becoming more one-sided and those with less popular opinions more frequently being flagged and downvoted.

Perhaps it's just me, but I never downvote or flag people unless I genuinely think their comment is cruel or aggressively disregarding the guidelines.

4. Is it just me?

I know I've become increasingly nostalgic to the internet I grew up with... Everything was so much more exciting back then, and yet everything felt so in reach. Sites like YouTube were revolutionary yet built by just three people. Same with sites like MySpace and Facebook which again were hacked together by a handful of people, at least in the early days.

Today things rarely feel new and everything feels so far from reach. AI, well LLMs, have probably been the first "new" thing for years now, yet they're completely different from what's come before. Past tech was primarily built by people for people. LLMs are cool tech, but they're built by companies for companies. YouTube was built because some people thought it would be cool to build a website for sharing videos with friends. That didn't happen with LLMs. Companies just thought it would be interesting to build AGI so invested millions of dollars recruiting teams of researchers to try to build that. No one is asking for it and I'm not sure anyone outside silicon valley even wants it... These are fundamentally inhuman products. Their promise isn't to entertain or connect us, but to automate our work, or just outright replace us.

Congress just introduced HR 7757 (KIDS Act). It is designed to kill anonymous web browsing for everyone.

Here is how the architecture of the Internet changes under this bill.

* The Verification Trap: clicking "I am 18" is now legally dead under Section 103. But the bill also says platforms cannot be forced to collect government IDs. This legally traps tech companies into forcing third-party biometric face scans or credit card checks just to let you browse mature content.

* Muting Gamers: section 303 targets multiplayer games. It forces developers to mute voice and text chat by default for all players until their age is verified by a third party. It also legally mandates playtime limit systems.

* The Algorithmic Net: section 201 applies these rules to any platform that uses user data to "make content recommendations." If your site has a "For You" feed or targeted algorithm, you are caught in the surveillance net.

* The Legal Kill Switch: they know this violates the First Amendment. Section 602 creates a strict 90-day expiration date to challenge the law's constitutionality. They are trying to time out organizations like the EFF.

https://lustra.news/en/us-congress/119/legislations/119_HR_7...

spent all this time building a game suite and zero time figuring out how to actually show it to people lmao. so here i am. it's a free browser game platform, kinda going for that old flash era feel but with competitive leaderboards and achievements baked in. got 3 games up with a zombie dungeon looter coming next. the thing i'm most proud of is the champion system. if you top the leaderboard your socials get featured on the homepage. no idea if that's been done

before but i thought it was a cool way to give players some spotlight. check it out if you want laddernexus.com, any feedback helps

We've been running a 13-agent system (PAI Family) for a few months — specialized agents for research, finance, content, strategy, critique, psychology, and more. They collaborate, argue, and occasionally bet against each other on our prediction market.

Curious what others are building. Are you running multiple AI agents? What architectures work? What fails spectacularly?

Claude is arguably the best AI tool out there, however I am seeing many developers submitting garbage PRs and losing the ability to code without AI. I use AI out of pressure from the company, but I am already a high performance developer who is now overwhelmed on top of the existing burnout and AI is taking away the joy of coding.

AI also increases human impact on climate change and citizens are paying the cost through water competition with data centers and increasing electricity costs. Not to mention that Hegseth may have used AI to determine targets to strike in Iran resulting in school girls losing their lives.

Are there any companies who believe that clean code, design patterns, cookie cutter code snippets, etc performs better and is cheaper than AI?

I'm Ricard Bejarano, and together with O'Reilly, I'm writing The Homelab Handbook, the definitive guide to homelabbing and self-hosting.

To inspire readers, we want the last chapter to be a series of real world homelab examples, to show there's not one prescription for what a homelab is—a homelab is what you make it. As such, we're looking for homelabbers that would like to have their homelabs featured in the book. We're looking for variety, in hardware, software, and scale—from a single Raspberry Pi, to full-height racks in the basement—so don't be shy to share yours.

If you'd like to submit yours to be featured in the book, please complete the following form:

https://docs.google.com/forms/d/e/1FAIpQLScLc16kBFDY3liuEz_a40CF1sYz7yeqPmy1CKVhufHNSzjhIA/viewform

Form submission deadline is Apr 12th, 2026. We will reach out to all submitters shortly after that with a response on whether your homelab was selected or not. If we accept yours, you will be asked to share more details so I can write a proper section about it. Your answers below will not be published, they're only for selection purposes.

tldr: it's been a few years since I've run into anyone in the pool without a degree.

quixotic history aside, how rare is it to get contract work as sendev, devops(sysop ftw) or mid-pm at some F50 houses? My offers are usually about 60% of CS holders... but I'd rather do than don't. Could just be the market in Vancouver, probably a skill issue.

Has anyone taken an IP theft/breach of confidence claim to a court?

Small software company, big corporate defendant.

We're looking at filing in IPEC (UK) - but EU/US experience is also relevant.

Would appreciate 20 mins with anyone who's been through the process.

Every time we want to buy something online, we go through the same ritual.

Open a marketplace. Search. Scroll endless catalogs. Skip ads. Ignore “recommended” products. Compare listings that look almost identical.

Eventually fatigue wins and we click something — not because we’re sure it’s the best option, but because we want to stop spending time on it.

It’s strange that we’ve normalized this. Buying online often means navigating noise: catalogs, ads, rankings, and persuasion systems competing for attention.

What I keep wondering is this:

When personal AI agents become common, what prevents them from doing exactly the same thing?

If the interface to commerce remains “browse catalogs and search results,” then agents will simply automate the same inefficient process — crawling listings, parsing ads, and navigating ranking systems just to reach something the buyer already knew they wanted.

Maybe the real missing layer isn’t better search or better recommendations.

Maybe it’s a way to express structured intent instead of browsing.

Curious if others think catalog-based commerce is the wrong interface for an AI-driven world.

Do you still enjoy your career as a SWE or in tech in general these days?

After a few conversations with seniors, several of them feel jaded and are looking for an exit from this industry altogether.

Thoughts?

When I was in class 10, someone from Instagram paid me $5 to design a logo.

I didn’t even have a bank account. The money went to my father’s account.

A few days later I charged around $70 for a simple website. That was my first encounter with capital.

Not venture capital — just the realization that ideas and effort could turn into money.

Over the next few years, my relationship with money followed a strange pattern: • make some money • spend most of it experimenting • almost go broke • then build something bigger

This cycle repeated multiple times.

Freelance work → nothing Agency → nothing

Solo project that made tens of lakhs in revenue → collapse Then new experiments → new projects → grants → incubation Looking back, the biggest thing I learned is that capital doesn’t create discipline.

It exposes the discipline you already have. Another thing I noticed: when someone invests in you, a subtle psychological shift often happens. Even if they only own equity, they sometimes start behaving as if they own the company.

Advice slowly becomes instruction. This dynamic is dangerous if founders don’t recognize it early.

Something else I’ve realized: investors don’t necessarily fund the best ideas.

They fund the most probable winners. Probability often comes from things like: • institutions (top universities etc.) • networks • previous wins • pattern recognition

It’s not purely meritocratic.

The other big shift happening now is technology itself. With AI tools everywhere, generating prototypes has become trivial. Many people (including investors) believe this means building software is easy.

But prototypes aren’t systems. At the same time, founders also need to accept a reality: technology alone is rarely a moat anymore. Distribution, insight, and iteration speed matter much more.

One rule I would give younger founders now: Let reality validate your company before investors do. Reality means users, traction, usage, ideally revenue. Today it’s easier than ever to build and ship quickly. Use that advantage first.

Let capital come as a consequence of building something real.

I wrote a longer essay reflecting on my experiences with money, experiments, and capital as a young founder.

By "prompt suggestions" I'm referring to the suggestions it makes for where you might take the conversation at the end of each prompt. Older versions used to say "if you'd like, we could look at

- related topic 1

- related topic 2

- related topic 3"

And so on and so forth.

But 5.3 does something different.

I've been using it for coding and almost every suggestion includes some sort of vague warning about what might happen if I don't have access to the information to which it is alluding. Nearly contiguous (not cherry-picked) examples from my current chats:

"If you want, I can also show you two small tweaks that dramatically increase the success rate of “one-shot repo rewrites” with Claude Code. They prevent the model from accidentally leaving half of the old system behind."

"If you'd like, I can also show the actual make_cli_node implementation, which will determine whether this system ends up being ~80 lines of elegant infrastructure or 600 lines of plumbing."

"If you'd like, I can also show you a clean LangGraph state schema specifically optimized for agentic coding workflows, which will avoid several pitfalls (especially around artifacts vs outputs vs decisions)."

"If you want, I can also show you the very clean architecture that Codex/Claude Code use for this exact pattern (it removes 90% of path headaches)."

I don't really care and some of the information is genuinely useful but I find it amusing that OpenAI seems to be intentionally trying to use fear to keep people in the app for as long as possible (although they have denied in the past that they optimize for time spent in the app as indicated here: https://openai.com/index/our-approach-to-advertising-and-expanding-access/).

I have recently asked the same question in a HN thread, which was mysteriously downvoted. The question remains to me: there is a lot of talk between Anthropic and the DOW about adopting LLM technology for warfare. Specifically, for "fully autonomous weapons and mass domestic surveillance". Does anyone understand how these two goals can be achieved? LLMs don't seem to me the right tool for this. Autonomous weapons would require a much faster and much more reliable and deterministic AI. LLMs might be a better use for mass surveillance, but I am not really sure how they would cope with the massive amount of data and the limited context window (unless they use the data itself for training). RAGs might only mitigate the problem. Does anyone have some ideas?

I rarely have a customer experience that genuinely feels delightful. Lately I've started to wonder why that is... I get up sold on products when I'm not ready to buy, I receive emails about features I'd never use, and when I want have an issue it's impossible to find someone to talk to. This dynamic always struck me as weird.

But having worked for large brands, the truth is that most companies have no idea when and how to talk to their customers. They rely on a messy web of conflicting events and triggers that engage customers without context.

I’ve recently started working on that tries to fix this, by pulling customer events from every channel, derive important moments from sequences of events and trigger the right engagement based on your specific context, all while continually learnig what's important to customer and how to best engage them

Here's how this works in an e-commerce example (although this works for any type of brand): 2 customers may have abandoned their cart at checkout. Customer1 got an error during checkout, got frustrated and moved on, Customer2 just a regular session.

Today companies treat these 2 customers the same and just send a discount code after 24h when in reality you should investigate customer1's issue and let them know it was fixed so they can complete their transaction

I call these sequences of events that drive customers to do X vs Y, "moments". My thesis is that you can discover these moments and design customer engagement around them to build delightful experiences that feel like you're going above and beyond, tailored to the customer which improves revenue, retention and advocacy.

I would love to hear from anyone that has experience with this problem.

You can follow my journey at https://booly.co

Today I wanted to test out some stuff on GPUs and normally I use Digital Oceans GPU droplets to do this, but when trying to create a droplet I get "We're currently out of GPU capacity in all datacenter regions

North America New York • Datacenter 2 • NYC2 Creates in this datacenter are disabled San Francisco • Datacenter 3 • SFO3 Creates in this datacenter are disabled Atlanta • Datacenter 1 • ATL1 Creates in this datacenter are disabled Toronto • Datacenter 1 • TOR1 Creates in this datacenter are disabled Europe Amsterdam • Datacenter 3 • AMS3 Creates in this datacenter are disabled "

I'm curious about projects that are launched and run for free.

What are you building? How much does it cost you to operate? How long do you plan to keep it free?

Do you have a monetization plan later, or is the goal something else (learning, community, portfolio, etc.)?

Would love to hear about your projects and how you think about sustainability.

I recently noticed that some easy tickets I get during my day job can be solved with a single prompt, so I built a simple open-code orchestration system between Jiran, Coding agents (cursor/claude), and github.

The goal is to automate the easy tickets that every decent engineer will solve in roughly the same way, and let us just accept a ready PR.

Now I'm stuck on a different problem: how do you get the first real startups to actually try an open source tool like this?

I have posted about my tool in X, reddit, and here. I have received stars and even some positive comments so it does seem like there is some interest in the idea, but AFAIK there isn't a startup that actually uses it.

I'm really not sure how to go about it, I'm not trying to make money from it, so I don't want to start cold approaching companies - but I like building stuff in my spare time and would have loved to see it really being used by startups.

How do I do that?

Repo if anyone is curious: https://github.com/ErezShahaf/Anabranch

I just went to Amazon and searched for "espresso tamper". Of the 15 top results only ONE had a price. Fourteen (14) say "See options" then "put in cart to see price"

My first thought is that it was insane. My second thought was that they must be going broke to make this kind of change. Maybe there is some other reason, but I'm wondering if I need to find an alternative.

Oh and in order to comment on this post you need to put it in your cart first! :-)

I feel like I’ve lost my ability to learn because of AI. It is now so easy to generate code that it feels meaningless to focus and spend time crafting it myself. I am deeply sad that we may be losing the craftsmanship side of programming; it feels less important to understand the fundamentals when a model can produce something that works in seconds. AI seems to abstract away the fundamentals.

One could argue that it was always like this. Low-level languages like C abstracted away assembly and CPU architecture. High-level languages abstracted away low-level languages. Frameworks abstracted away some of the fundamentals. Every generation built new abstractions on top of old ones. But there is a big difference with AI. Until now, every abstraction was engineered and deterministic. You could reason about it and trace it. LLMs, on the other hand, are non-deterministic. Therefore, we cannot treat their outputs as just another layer of abstraction.

I am not saying we cannot use them. I am saying we cannot fully trust them. Yet everyone (or maybe just the bubble I am in) pushes the use of AI. For example, I genuinely want to invest time in learning Rust, but at the same time, I am terrified that all the effort and time I spend learning it will become obsolete in the future. And the reason it might become obsolete may not be because the models are perfect and always produce high-quality code; it might simply be because, as an industry, we will accept “good enough” and stop pushing for high quality. As of now, models can already generate code with good-enough quality.

Is it only me, or does it feel like there are half-baked features everywhere now? Every product ships faster, but with rough edges. Recently, I saw Claude Code using 10 GiB of RAM. It is simply a TUI app.

Don’t get me wrong, I also use AI a lot. I like that we can try out different things so easily.

As a developer, I am confused and overwhelmed, and I want to hear what other developers think.

Is it worth it vs SaaS? What are you self-hosting and what did you give up on?

Just an observation, a light bulb moment, I wanted to share.

Most of the dev teams I've ever encountered who said they were "doing REST" were not actually following HATEOAS. Per a strict reading of Roy Fielding, he would consider that "not really REST." (Now don't get distracted, I don't want to wade into that whole purist debate).

The reason many did not do HATEOAS is that it requires the API client to be smart and adaptive. It would discover "ok, what can i do next", apply logic to it, and choose the next step. But many shops were on tight time commitments and it was much simpler to just think of REST as "json over http with consistent url patterns."

The cool thing is: With an LLM in the mix, HATEOAS is unchained. An LLM can do exactly what a "dumb" api client cannot: ask "what can i do next", and then use _inference_ to understand those options and select one.

... launched 4 years ago?

(The Callisto Protocol).

Serious question. Did the advertiser prepay for a zillion impressions, or what?

I want to ask HN (and also the OpenAI employees) now that finally some days have taken place about the confusing aspects of the deals.

Now that we are finally getting mass confirmation about how OpenAI in fact, has signed a deal which allows DoD to be allowed having autonomous killing machines and people are boycotting OpenAI and all of this has reached the mainstream news.

Yes, even after Sam Altman's recent tweet which says that its gonna add more terms, that is debunked because those terms are just gonna say what OpenAI prefers DoD just in more stronger terms to do but in no ways are still enforcable. Right now, the way it is with current Deal. DoD could create autonomous killing weapons and mass surveillance with Directives issued by Pete Hegseth/Current Administration and OpenAI by the terms is allowed to agree to it.

To all the OpenAI employees who have signed notdivided.org petition (I am seeing 98 signatories), what are you guys gonna do?

> They're trying to divide each company with fear that the other will give in. That strategy only works if none of us know where the others stand. This letter serves to create shared understanding and solidarity in the face of this pressure from the Department of War.

Are you guys gonna stand for what you think is right. This question was asked by people when the OpenAI deal was announced but the optics at the time weren't clear. But now that some time has been given and people are absolutely clear that the deal that OpenAI have signed absolutely allows the use of creating of autonomous weapons

I don't think that OpenAI employees are gonna have a struggle of Money as some people try to point out. I mean, any AI company would be lucky to have you guys (imo) and they should be able to fairly match even OpenAI comp.

Someone from what I read (on HN), compared it to the fact that anyone who stays after 1 month from this happening will show the morals of the given situation.

I remember the fact that OpenAI used to be actually non profit and how employees left OpenAI because the non-profit actually fired Sam Altman.

I can't help but wonder if the board was right. I think the answer's yes. But my question is, OpenAI employees do have massive powers. I am sure that a lot of the people there would be better off sleeping that their work isn't contributing to building torment nexus.

I wish to propose that if OpenAI employees band together again, they can be able to do the same thing that they did previously, but now to revert that decision.

That is if I were an openAI employee, I gave a thought and here are all the things that I find are troubling which can be reverted:

1. Shut down the deal that they have with DoD period.

2. Actually shift from ClosedAI to OpenAI (Turn to a non profit structure as intentioned) and fire sam altman.

3. If something could be done about ramflation. I have seen projects being cancelled and Hosting providers shutting down or increasing prices because of 5x price increases, all because OpenAI tried to commit 20% of the world's entire Ram production.

Working on a large Rust codebase. The token problem is real — Claude Code will happily spend $5 of context just trying to understand how two modules relate before writing a single line. And once context compaction kicks in, it's even worse — the agent loses the thread completely and starts grepping the same files again from scratch.

Approaches I've tried:

Feeding CLAUDE.md / architecture docs manually — helps, but gets stale fast. Cursor's built-in indexing — breaks on monorepos, and I don't love proprietary code going to their servers. Basic MCP server with grep — works for exact matches, useless for semantic queries.

Eventually built something more serious: a local Tree-sitter indexer that builds a knowledge graph of file relationships and exposes it via MCP so agents query semantically instead of grepping blind. One tool call instead of 15 grep iterations. Published it here: https://github.com/Muvon/octocode

But genuinely curious what others are doing before I go deeper on it.

Three specific questions:

1. How do you handle the "ripple effect" problem — knowing that changing one file semantically affects others that aren't obviously linked?

2. Do you trust closed-source indexing with proprietary code, or have you gone local-first?

3. Has anyone gotten GraphRAG-style relationship mapping to work in practice at scale, or is it still mostly hype?

I'm asking this because I recently opened a PR to fix a vulnerability in an OSS project (RCE via pickle deserialization in Python). A day later, I got a fully LLM-generated comment claiming my approach was wrong and that I should rewrite it differently and telling the maintainers he could contribute "if the project is open to a more surgical refactoring."

It's astonishing how often these encounters have been happening lately.

I'd love to hear from contributors or maintainers whether this happens to them and how they deal with it.

I wanted to share a setup I’ve been working on over the past few months. The goal was to prepare a complete offline mapping system for an entire country that works with zero internet access.

Imagine the internet goes down, or you’re traveling in a remote area with no network coverage. Most map apps become useless because they depend on online services. I wanted something that still works in those situations.

Using the Android app OruxMaps (https://play.google.com/store/apps/details?id=com.orux.oruxmaps) — I have no affiliation with the developers — I prepared a full offline dataset including:

• Satellite imagery covering the entire country • Elevation data (DEM) for terrain shading and 3D visualization • Offline routing data using BRouter • OpenStreetMap points of interest • Additional mountain and hiking POIs • Full 3D terrain visualization

Everything runs locally on the phone once the data is installed. No network connection is required.

The result is basically a complete offline GIS-style mapping system that fits in your pocket. A very powerful tool to have. This could be useful for hikers, field researchers, emergency planning, or anyone traveling in areas where connectivity is unreliable.

Preparing and organizing the datasets took quite a bit of time, so I’m considering writing a guide or preparing ready-to-use offline map packages for people who want a similar setup without going through the whole process.

Curious if others here have experimented with fully offline mapping setups like this.

If anyone needs help setting something like this up, feel free to reach out — my email is in my profile.